Overview

Dataset statistics

Number of variables17
Number of observations387
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory51.5 KiB
Average record size in memory136.3 B

Variable types

Numeric13
Categorical4

Warnings

close is highly correlated with dif_M50M180High correlation
RSI_14 is highly correlated with Z_30High correlation
Z_30 is highly correlated with RSI_14High correlation
dif_M50M180 is highly correlated with closeHigh correlation
close has unique values Unique
RSI_14 has unique values Unique
ROC_2 has unique values Unique
Z_30 has unique values Unique
dif_M50M180 has unique values Unique
ratio_M50M180 has unique values Unique
dif_M5M20 has unique values Unique
ratio_M5M20 has unique values Unique
dif_M20M50 has unique values Unique
ratio_M20M50 has unique values Unique
ratio_MACDh_12_26_9 has unique values Unique
obv_pct_delta has unique values Unique

Reproduction

Analysis started2021-04-06 04:01:30.337984
Analysis finished2021-04-06 04:02:15.622434
Duration45.28 seconds
Software versionpandas-profiling v2.11.0
Download configurationconfig.yaml

Variables

close
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct387
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean323.418635
Minimum44.64199829
Maximum883.0900269
Zeros0
Zeros (%)0.0%
Memory size3.1 KiB
2021-04-05T23:02:15.863451image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum44.64199829
5-th percentile51.39059906
Q1106.6080017
median201.8699951
Q3449.5750122
95-th percentile823.1479797
Maximum883.0900269
Range838.4480286
Interquartile range (IQR)342.9670105

Descriptive statistics

Standard deviation250.9303487
Coefficient of variation (CV)0.775868554
Kurtosis-0.7627792787
Mean323.418635
Median Absolute Deviation (MAD)135.8819962
Skewness0.7192536512
Sum125163.0117
Variance62966.03989
MonotocityNot monotonic
2021-04-05T23:02:16.146476image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
137.34399411
 
0.3%
297.91598511
 
0.3%
755.97998051
 
0.3%
486.64001461
 
0.3%
188.13400271
 
0.3%
297.49798581
 
0.3%
418.32000731
 
0.3%
187.05599981
 
0.3%
145.02999881
 
0.3%
699.59997561
 
0.3%
Other values (377)377
97.4%
ValueCountFrequency (%)
44.641998291
0.3%
45.740001681
0.3%
46.28599931
0.3%
46.605998991
0.3%
47.543998721
0.3%
ValueCountFrequency (%)
883.09002691
0.3%
880.79998781
0.3%
880.02001951
0.3%
872.7899781
0.3%
864.15997311
0.3%

RSI_14
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct387
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean61.25107956
Minimum24.28249643
Maximum93.05754452
Zeros0
Zeros (%)0.0%
Memory size3.1 KiB
2021-04-05T23:02:16.423494image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum24.28249643
5-th percentile38.98945605
Q151.75180877
median60.67429265
Q372.08953983
95-th percentile81.98148611
Maximum93.05754452
Range68.77504809
Interquartile range (IQR)20.33773105

Descriptive statistics

Standard deviation13.47590739
Coefficient of variation (CV)0.2200109367
Kurtosis-0.5223758431
Mean61.25107956
Median Absolute Deviation (MAD)10.47695295
Skewness-0.184449461
Sum23704.16779
Variance181.60008
MonotocityNot monotonic
2021-04-05T23:02:16.725520image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
59.90238061
 
0.3%
60.230093811
 
0.3%
57.889224291
 
0.3%
56.243154481
 
0.3%
61.729801351
 
0.3%
46.250368271
 
0.3%
64.859350721
 
0.3%
54.779787011
 
0.3%
72.587398221
 
0.3%
59.733461031
 
0.3%
Other values (377)377
97.4%
ValueCountFrequency (%)
24.282496431
0.3%
26.800441351
0.3%
27.653171721
0.3%
28.443503981
0.3%
29.576561141
0.3%
ValueCountFrequency (%)
93.057544521
0.3%
90.595728131
0.3%
86.182143461
0.3%
85.273243291
0.3%
85.249714151
0.3%

INC_2
Categorical

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
1
232 
0
155 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters387
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row1
ValueCountFrequency (%)
1232
59.9%
0155
40.1%
2021-04-05T23:02:17.284561image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-04-05T23:02:17.503579image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
1232
59.9%
0155
40.1%

Most occurring characters

ValueCountFrequency (%)
1232
59.9%
0155
40.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number387
100.0%

Most frequent character per category

ValueCountFrequency (%)
1232
59.9%
0155
40.1%

Most occurring scripts

ValueCountFrequency (%)
Common387
100.0%

Most frequent character per script

ValueCountFrequency (%)
1232
59.9%
0155
40.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII387
100.0%

Most frequent character per block

ValueCountFrequency (%)
1232
59.9%
0155
40.1%

ROC_2
Real number (ℝ)

UNIQUE

Distinct387
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.628300091
Minimum-21.29815696
Maximum36.35120335
Zeros0
Zeros (%)0.0%
Memory size3.1 KiB
2021-04-05T23:02:17.736596image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-21.29815696
5-th percentile-9.449591187
Q1-2.090018523
median1.085776951
Q34.742501507
95-th percentile13.54410617
Maximum36.35120335
Range57.64936031
Interquartile range (IQR)6.83252003

Descriptive statistics

Standard deviation7.355380335
Coefficient of variation (CV)4.517214225
Kurtosis2.363779091
Mean1.628300091
Median Absolute Deviation (MAD)3.46401167
Skewness0.4654430768
Sum630.1521354
Variance54.10161987
MonotocityNot monotonic
2021-04-05T23:02:18.021619image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.1163416391
 
0.3%
9.3533083891
 
0.3%
1.0857769511
 
0.3%
9.600515881
 
0.3%
3.8788954651
 
0.3%
-0.11552277891
 
0.3%
13.63304681
 
0.3%
14.319899081
 
0.3%
-0.32186806421
 
0.3%
5.747921311
 
0.3%
Other values (377)377
97.4%
ValueCountFrequency (%)
-21.298156961
0.3%
-20.601195741
0.3%
-18.867323971
0.3%
-18.839729321
0.3%
-16.084692031
0.3%
ValueCountFrequency (%)
36.351203351
0.3%
28.840109921
0.3%
24.168178351
0.3%
23.890051241
0.3%
22.502976791
0.3%

PSL_3
Categorical

Distinct4
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
66.66666666666667
164 
33.333333333333336
128 
100.0
68 
0.0
27 

Length

Max length18
Median length17
Mean length14.24547804
Min length3

Characters and Unicode

Total characters5513
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row66.66666666666667
2nd row0.0
3rd row0.0
4th row33.333333333333336
5th row66.66666666666667
ValueCountFrequency (%)
66.66666666666667164
42.4%
33.333333333333336128
33.1%
100.068
17.6%
0.027
 
7.0%
2021-04-05T23:02:18.575658image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-04-05T23:02:18.799675image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
66.66666666666667164
42.4%
33.333333333333336128
33.1%
100.068
17.6%
0.027
 
7.0%

Most occurring characters

ValueCountFrequency (%)
62588
46.9%
32048
37.1%
.387
 
7.0%
0258
 
4.7%
7164
 
3.0%
168
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number5126
93.0%
Other Punctuation387
 
7.0%

Most frequent character per category

ValueCountFrequency (%)
62588
50.5%
32048
40.0%
0258
 
5.0%
7164
 
3.2%
168
 
1.3%
ValueCountFrequency (%)
.387
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common5513
100.0%

Most frequent character per script

ValueCountFrequency (%)
62588
46.9%
32048
37.1%
.387
 
7.0%
0258
 
4.7%
7164
 
3.0%
168
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII5513
100.0%

Most frequent character per block

ValueCountFrequency (%)
62588
46.9%
32048
37.1%
.387
 
7.0%
0258
 
4.7%
7164
 
3.0%
168
 
1.2%

CDL_DOJI_3_0.1
Categorical

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
0.0
341 
1.0
46 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1161
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.0341
88.1%
1.046
 
11.9%
2021-04-05T23:02:19.319715image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-04-05T23:02:19.701745image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0341
88.1%
1.046
 
11.9%

Most occurring characters

ValueCountFrequency (%)
0728
62.7%
.387
33.3%
146
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number774
66.7%
Other Punctuation387
33.3%

Most frequent character per category

ValueCountFrequency (%)
0728
94.1%
146
 
5.9%
ValueCountFrequency (%)
.387
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1161
100.0%

Most frequent character per script

ValueCountFrequency (%)
0728
62.7%
.387
33.3%
146
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1161
100.0%

Most frequent character per block

ValueCountFrequency (%)
0728
62.7%
.387
33.3%
146
 
4.0%

TRUERANGE_1
Real number (ℝ≥0)

Distinct385
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.70036297
Minimum0.7580032349
Maximum115.0900269
Zeros0
Zeros (%)0.0%
Memory size3.1 KiB
2021-04-05T23:02:19.928763image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.7580032349
5-th percentile1.438804245
Q15.104991913
median14.29998779
Q328.59500122
95-th percentile60.71601562
Maximum115.0900269
Range114.3320236
Interquartile range (IQR)23.49000931

Descriptive statistics

Standard deviation18.80553284
Coefficient of variation (CV)0.9545779873
Kurtosis2.820325757
Mean19.70036297
Median Absolute Deviation (MAD)10.34397888
Skewness1.546642296
Sum7624.04047
Variance353.6480652
MonotocityNot monotonic
2021-04-05T23:02:20.217783image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.5979919432
 
0.5%
68.450012212
 
0.5%
9.5500030521
 
0.3%
18.799987791
 
0.3%
14.299987791
 
0.3%
2.2299957281
 
0.3%
32.986022951
 
0.3%
7.6160049441
 
0.3%
25.090026861
 
0.3%
5.0439987181
 
0.3%
Other values (375)375
96.9%
ValueCountFrequency (%)
0.75800323491
0.3%
0.88800048831
0.3%
0.95800018311
0.3%
0.99199676511
0.3%
1.0019989011
0.3%
ValueCountFrequency (%)
115.09002691
0.3%
95.51
0.3%
88.440002441
0.3%
88.350036621
0.3%
76.400024411
0.3%

Z_30
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct387
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8730141851
Minimum-3.261496968
Maximum3.904008176
Zeros0
Zeros (%)0.0%
Memory size3.1 KiB
2021-04-05T23:02:20.501807image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-3.261496968
5-th percentile-1.558792975
Q10.09499178136
median1.067054453
Q31.726419746
95-th percentile2.823665476
Maximum3.904008176
Range7.165505144
Interquartile range (IQR)1.631427964

Descriptive statistics

Standard deviation1.294462143
Coefficient of variation (CV)1.482750412
Kurtosis0.2395247494
Mean0.8730141851
Median Absolute Deviation (MAD)0.7883195004
Skewness-0.6048211499
Sum337.8564896
Variance1.67563224
MonotocityNot monotonic
2021-04-05T23:02:20.791827image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-2.1021924361
 
0.3%
1.1661912861
 
0.3%
1.0386388591
 
0.3%
1.0319141881
 
0.3%
-0.66946963831
 
0.3%
-0.44571771191
 
0.3%
-1.1560508221
 
0.3%
0.67280751711
 
0.3%
0.28108405751
 
0.3%
1.6270860471
 
0.3%
Other values (377)377
97.4%
ValueCountFrequency (%)
-3.2614969681
0.3%
-2.9955192641
0.3%
-2.7045974351
0.3%
-2.627835981
0.3%
-2.5679205621
0.3%
ValueCountFrequency (%)
3.9040081761
0.3%
3.8869142251
0.3%
3.3870569721
0.3%
3.3769655661
0.3%
3.3344659471
0.3%

dif_M50M180
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct387
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean96.87156664
Minimum-4.182070923
Maximum288.0229891
Zeros0
Zeros (%)0.0%
Memory size3.1 KiB
2021-04-05T23:02:21.080853image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-4.182070923
5-th percentile-2.576017868
Q130.7647752
median54.28502836
Q3157.2179273
95-th percentile272.3837408
Maximum288.0229891
Range292.20506
Interquartile range (IQR)126.4531521

Descriptive statistics

Standard deviation85.62697712
Coefficient of variation (CV)0.8839227039
Kurtosis-0.6440449158
Mean96.87156664
Median Absolute Deviation (MAD)50.75145503
Skewness0.7158587153
Sum37489.29629
Variance7331.979211
MonotocityNot monotonic
2021-04-05T23:02:21.367874image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24.721504111
 
0.3%
52.026335121
 
0.3%
49.555150871
 
0.3%
74.288512191
 
0.3%
163.22765191
 
0.3%
34.300630761
 
0.3%
168.65714971
 
0.3%
6.6421310851
 
0.3%
285.73767751
 
0.3%
88.441502921
 
0.3%
Other values (377)377
97.4%
ValueCountFrequency (%)
-4.1820709231
0.3%
-4.1125153771
0.3%
-4.1095087691
0.3%
-4.0857287811
0.3%
-4.0282954281
0.3%
ValueCountFrequency (%)
288.02298911
0.3%
287.65101081
0.3%
287.45429981
0.3%
286.99188841
0.3%
285.93955561
0.3%

ratio_M50M180
Real number (ℝ)

UNIQUE

Distinct387
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.006827305
Minimum-1.545331252
Maximum2.622834071
Zeros0
Zeros (%)0.0%
Memory size3.1 KiB
2021-04-05T23:02:21.659897image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-1.545331252
5-th percentile0.9695989985
Q10.9935962965
median1.00916944
Q31.023221575
95-th percentile1.043606251
Maximum2.622834071
Range4.168165323
Interquartile range (IQR)0.02962527884

Descriptive statistics

Standard deviation0.1695262429
Coefficient of variation (CV)0.1683766838
Kurtosis155.752086
Mean1.006827305
Median Absolute Deviation (MAD)0.01509363461
Skewness-6.654066178
Sum389.6421672
Variance0.02873914702
MonotocityNot monotonic
2021-04-05T23:02:21.924914image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.0182353891
 
0.3%
0.99730314331
 
0.3%
1.0072136841
 
0.3%
1.0202230371
 
0.3%
1.0245128841
 
0.3%
1.0277805361
 
0.3%
1.0361086941
 
0.3%
0.97158374131
 
0.3%
0.97185221591
 
0.3%
0.96990867011
 
0.3%
Other values (377)377
97.4%
ValueCountFrequency (%)
-1.5453312521
0.3%
0.29064406351
0.3%
0.58248071271
0.3%
0.67078032261
0.3%
0.74759056771
0.3%
ValueCountFrequency (%)
2.6228340711
0.3%
1.7093654211
0.3%
1.3962693911
0.3%
1.2969128671
0.3%
1.2421548721
0.3%

dif_M5M20
Real number (ℝ)

UNIQUE

Distinct387
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.73628779
Minimum-108.6974945
Maximum123.871994
Zeros0
Zeros (%)0.0%
Memory size3.1 KiB
2021-04-05T23:02:22.197935image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-108.6974945
5-th percentile-37.39760983
Q1-1.527900124
median6.834898376
Q321.75069885
95-th percentile82.31366119
Maximum123.871994
Range232.5694885
Interquartile range (IQR)23.27859898

Descriptive statistics

Standard deviation34.57719863
Coefficient of variation (CV)2.946178489
Kurtosis2.110278701
Mean11.73628779
Median Absolute Deviation (MAD)11.41439514
Skewness0.1646948634
Sum4541.943375
Variance1195.582665
MonotocityNot monotonic
2021-04-05T23:02:22.469956image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.042700386051
 
0.3%
15.793699651
 
0.3%
-92.0729951
 
0.3%
6.1525995251
 
0.3%
20.911598591
 
0.3%
-16.458398441
 
0.3%
-0.55620098111
 
0.3%
40.139001461
 
0.3%
12.740404511
 
0.3%
6.2557991031
 
0.3%
Other values (377)377
97.4%
ValueCountFrequency (%)
-108.69749451
0.3%
-101.77749331
0.3%
-92.0729951
0.3%
-91.616494751
0.3%
-89.7354951
0.3%
ValueCountFrequency (%)
123.8719941
0.3%
119.07649841
0.3%
114.91499331
0.3%
104.02149051
0.3%
98.675001531
0.3%

ratio_M5M20
Real number (ℝ)

UNIQUE

Distinct387
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8226002666
Minimum-78.56781286
Maximum9.615056317
Zeros0
Zeros (%)0.0%
Memory size3.1 KiB
2021-04-05T23:02:22.785146image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-78.56781286
5-th percentile-0.1909566522
Q10.8422459976
median0.9960962066
Q31.179563961
95-th percentile2.139176592
Maximum9.615056317
Range88.18286918
Interquartile range (IQR)0.337317963

Descriptive statistics

Standard deviation4.314882983
Coefficient of variation (CV)5.245418897
Kurtosis299.4141965
Mean0.8226002666
Median Absolute Deviation (MAD)0.1669953532
Skewness-16.34594319
Sum318.3463032
Variance18.61821516
MonotocityNot monotonic
2021-04-05T23:02:23.246180image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.67891797441
 
0.3%
0.2944754541
 
0.3%
1.4051321161
 
0.3%
1.0574746291
 
0.3%
0.8407583351
 
0.3%
0.92360635881
 
0.3%
1.2612811871
 
0.3%
0.93334638781
 
0.3%
0.85988228191
 
0.3%
0.90122573891
 
0.3%
Other values (377)377
97.4%
ValueCountFrequency (%)
-78.567812861
0.3%
-16.781923941
0.3%
-7.25484751
0.3%
-3.9160562341
0.3%
-3.1818371391
0.3%
ValueCountFrequency (%)
9.6150563171
0.3%
8.4793603871
0.3%
7.6964753191
0.3%
6.4388132051
0.3%
6.131186311
0.3%

dif_M20M50
Real number (ℝ)

UNIQUE

Distinct387
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.58852656
Minimum-97.58369812
Maximum135.0095978
Zeros0
Zeros (%)0.0%
Memory size3.1 KiB
2021-04-05T23:02:23.521200image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-97.58369812
5-th percentile-38.3576504
Q13.917390003
median19.53171783
Q344.06187851
95-th percentile116.1505608
Maximum135.0095978
Range232.5932959
Interquartile range (IQR)40.14448851

Descriptive statistics

Standard deviation44.64519966
Coefficient of variation (CV)1.744735069
Kurtosis1.191991803
Mean25.58852656
Median Absolute Deviation (MAD)17.77437824
Skewness0.1501969523
Sum9902.759778
Variance1993.193853
MonotocityNot monotonic
2021-04-05T23:02:23.804222image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
62.794458921
 
0.3%
47.939237671
 
0.3%
8.4728598791
 
0.3%
25.183641511
 
0.3%
2.8538198851
 
0.3%
27.378061071
 
0.3%
17.886961061
 
0.3%
14.843500141
 
0.3%
-1.279879991
 
0.3%
35.996900021
 
0.3%
Other values (377)377
97.4%
ValueCountFrequency (%)
-97.583698121
0.3%
-96.887898561
0.3%
-96.333800661
0.3%
-94.364700321
0.3%
-94.244298711
0.3%
ValueCountFrequency (%)
135.00959781
0.3%
134.9429981
0.3%
134.03409851
0.3%
132.28729861
0.3%
132.17169741
0.3%

ratio_M20M50
Real number (ℝ)

UNIQUE

Distinct387
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.9794647528
Minimum-18.94651159
Maximum8.443957764
Zeros0
Zeros (%)0.0%
Memory size3.1 KiB
2021-04-05T23:02:24.098247image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-18.94651159
5-th percentile0.713317086
Q10.9600869456
median1.011585182
Q31.055065628
95-th percentile1.283952276
Maximum8.443957764
Range27.39046935
Interquartile range (IQR)0.09497868242

Descriptive statistics

Standard deviation1.153518988
Coefficient of variation (CV)1.177703419
Kurtosis236.5477929
Mean0.9794647528
Median Absolute Deviation (MAD)0.04591242071
Skewness-12.61035937
Sum379.0528593
Variance1.330606056
MonotocityNot monotonic
2021-04-05T23:02:24.390267image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.0115851821
 
0.3%
0.96199081711
 
0.3%
0.98059720121
 
0.3%
1.0597653841
 
0.3%
-1.4976002561
 
0.3%
0.98054940651
 
0.3%
1.0060807781
 
0.3%
1.4562498251
 
0.3%
1.0398229091
 
0.3%
0.90536732281
 
0.3%
Other values (377)377
97.4%
ValueCountFrequency (%)
-18.946511591
0.3%
-1.6746904311
0.3%
-1.4976002561
0.3%
-0.41212007521
0.3%
-0.39114459721
0.3%
ValueCountFrequency (%)
8.4439577641
0.3%
4.6756184051
0.3%
4.4974059711
0.3%
2.555662451
0.3%
2.5346674191
0.3%

ratio_MACDh_12_26_9
Real number (ℝ)

UNIQUE

Distinct387
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.1886680457
Minimum-420.4361362
Maximum31.84985671
Zeros0
Zeros (%)0.0%
Memory size3.1 KiB
2021-04-05T23:02:24.691288image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-420.4361362
5-th percentile-0.7620869848
Q10.7043996003
median0.9550213771
Q31.180203162
95-th percentile2.442967542
Maximum31.84985671
Range452.2859929
Interquartile range (IQR)0.4758035622

Descriptive statistics

Standard deviation21.7187725
Coefficient of variation (CV)-115.1163273
Kurtosis365.9352219
Mean-0.1886680457
Median Absolute Deviation (MAD)0.2378729395
Skewness-18.89654265
Sum-73.01453368
Variance471.7050791
MonotocityNot monotonic
2021-04-05T23:02:24.995314image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.68806154221
 
0.3%
0.83014039291
 
0.3%
0.64939935891
 
0.3%
0.89856928011
 
0.3%
0.46520864961
 
0.3%
1.0317684121
 
0.3%
5.016142651
 
0.3%
0.60918708481
 
0.3%
0.96929537981
 
0.3%
0.78269164551
 
0.3%
Other values (377)377
97.4%
ValueCountFrequency (%)
-420.43613621
0.3%
-54.289536741
0.3%
-10.689466581
0.3%
-7.124250931
0.3%
-6.8067551121
0.3%
ValueCountFrequency (%)
31.849856711
0.3%
12.381583091
0.3%
11.060223341
0.3%
7.6327693561
0.3%
6.7508377531
0.3%

obv_pct_delta
Real number (ℝ)

UNIQUE

Distinct387
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.006151569282
Minimum-0.08079053699
Maximum0.1430353405
Zeros0
Zeros (%)0.0%
Memory size3.1 KiB
2021-04-05T23:02:25.304335image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-0.08079053699
5-th percentile-0.04286675275
Q1-0.004544721816
median0.001819530926
Q30.02072529027
95-th percentile0.05366135604
Maximum0.1430353405
Range0.2238258774
Interquartile range (IQR)0.02527001209

Descriptive statistics

Standard deviation0.02729207374
Coefficient of variation (CV)4.436603489
Kurtosis1.887937992
Mean0.006151569282
Median Absolute Deviation (MAD)0.0131444853
Skewness0.3477298131
Sum2.380657312
Variance0.0007448572891
MonotocityNot monotonic
2021-04-05T23:02:25.594358image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.080790536991
 
0.3%
-0.007903722381
 
0.3%
-0.021744182561
 
0.3%
-0.0018610362031
 
0.3%
0.0015195645861
 
0.3%
0.02180936731
 
0.3%
0.00052921213931
 
0.3%
-0.042686071041
 
0.3%
0.040494793751
 
0.3%
0.037177682531
 
0.3%
Other values (377)377
97.4%
ValueCountFrequency (%)
-0.080790536991
0.3%
-0.064231335521
0.3%
-0.063278727191
0.3%
-0.058912727041
0.3%
-0.057907541821
0.3%
ValueCountFrequency (%)
0.14303534051
0.3%
0.083148682971
0.3%
0.079099733341
0.3%
0.073739089831
0.3%
0.07351996211
0.3%

PL
Categorical

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
1.0
220 
0.0
167 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1161
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row1.0
5th row0.0
ValueCountFrequency (%)
1.0220
56.8%
0.0167
43.2%
2021-04-05T23:02:26.158403image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-04-05T23:02:26.392421image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0220
56.8%
0.0167
43.2%

Most occurring characters

ValueCountFrequency (%)
0554
47.7%
.387
33.3%
1220
 
18.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number774
66.7%
Other Punctuation387
33.3%

Most frequent character per category

ValueCountFrequency (%)
0554
71.6%
1220
 
28.4%
ValueCountFrequency (%)
.387
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1161
100.0%

Most frequent character per script

ValueCountFrequency (%)
0554
47.7%
.387
33.3%
1220
 
18.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII1161
100.0%

Most frequent character per block

ValueCountFrequency (%)
0554
47.7%
.387
33.3%
1220
 
18.9%

Interactions

2021-04-05T23:01:34.503002image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-05T23:01:34.861024image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-05T23:01:35.109041image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-05T23:01:35.355059image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-05T23:01:35.680087image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-05T23:01:35.922102image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-05T23:01:36.155122image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-05T23:01:36.391140image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-05T23:01:36.627159image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-05T23:01:36.867184image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-05T23:01:37.103195image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-05T23:01:37.335211image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-05T23:01:37.565231image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-05T23:01:37.810248image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-05T23:01:38.056265image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-05T23:01:38.304287image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-05T23:01:38.563308image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-05T23:01:38.858842image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-05T23:01:39.165862image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-05T23:01:39.434878image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-05T23:01:39.732902image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-05T23:01:39.982922image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-05T23:01:40.230940image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-05T23:01:40.478958image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-05T23:01:40.733979image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-05T23:01:40.972998image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-05T23:01:41.225017image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-05T23:01:41.475036image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-05T23:01:41.730057image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-05T23:01:41.997076image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-05T23:01:42.409104image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-05T23:01:42.657122image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-05T23:01:42.899141image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-05T23:01:43.139161image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-05T23:01:43.388178image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-05T23:01:43.631198image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-05T23:01:43.872215image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-05T23:01:44.118235image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-05T23:01:44.378257image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-05T23:01:44.625273image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-05T23:01:44.884292image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-05T23:01:45.143314image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-05T23:01:45.383331image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-05T23:01:45.615352image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-05T23:01:45.855367image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-05T23:01:46.183392image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-05T23:01:46.560089image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-05T23:01:46.906115image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-05T23:01:47.228144image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-05T23:01:47.530167image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-05T23:01:47.867190image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-05T23:01:48.121211image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-05T23:01:48.372232image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-05T23:01:48.638248image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-05T23:01:48.887269image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-05T23:01:49.288301image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-05T23:01:49.543319image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-05T23:01:49.798339image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-05T23:01:50.059357image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-05T23:01:50.317377image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-05T23:01:50.575397image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-05T23:01:50.818415image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-05T23:01:51.113438image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-05T23:01:51.374458image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-05T23:01:51.645479image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-05T23:01:51.925499image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-05T23:01:52.178520image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-05T23:01:52.442542image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-05T23:01:52.713562image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-05T23:01:52.981580image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-05T23:01:53.243907image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-05T23:01:53.490927image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-05T23:01:53.779950image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-05T23:01:54.035969image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-05T23:01:54.313988image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-05T23:01:54.586006image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-05T23:01:54.871030image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-05T23:01:55.133048image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-05T23:01:55.368070image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-05T23:01:55.603085image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-05T23:01:55.871107image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-05T23:01:56.258135image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-05T23:01:56.517156image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-05T23:01:56.745174image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-05T23:01:56.984191image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-05T23:01:57.212209image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-05T23:01:57.451225image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-05T23:01:57.717246image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-05T23:01:57.963269image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-05T23:01:58.204284image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-05T23:01:58.441303image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-05T23:01:58.673320image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-05T23:01:58.926338image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-05T23:01:59.190359image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
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Correlations

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Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-04-05T23:02:27.200482image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-04-05T23:02:27.607512image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-04-05T23:02:28.006543image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-04-05T23:02:28.347570image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

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A simple visualization of nullity by column.
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Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

closeRSI_14INC_2ROC_2PSL_3CDL_DOJI_3_0.1TRUERANGE_1Z_30dif_M50M180ratio_M50M180dif_M5M20ratio_M5M20dif_M20M50ratio_M20M50ratio_MACDh_12_26_9obv_pct_deltaPL
0846.64001567.6560100-0.44799866.6666670.019.3800051.237258236.6568331.02014666.1764980.868874129.9550981.014104-420.436136-0.0001201.0
162.66199972.6497350-0.5396660.0000000.01.3360021.5945110.5795822.6228348.6149000.8592134.9195401.1017070.809223-0.0151650.0
289.01400028.4435040-20.6011960.0000000.020.889999-2.70459754.5659790.996572-31.1497001.10530112.7036200.7787681.177960-0.0571190.0
3635.61999543.40381312.73310233.3333330.046.649963-0.768101196.0644990.971852-24.4269931.127747-91.7828000.9863540.2589150.0028591.0
4844.98999067.46574010.05209966.6666670.014.2999881.311024231.9832551.01960276.1634950.867426128.1476991.0114900.0020330.0013630.0
5742.02002041.69253813.85164733.3333330.050.830017-1.821898283.9586340.997303-66.5179901.06692538.0263980.8026020.892824-0.0079041.0
6377.41799974.325487114.319899100.0000000.017.6520083.117012101.8310891.02022334.9948011.98000540.1735210.9862191.8053590.0486261.0
7140.26400852.5143850-12.39084933.3333330.019.7680050.65013635.9525850.96438917.0546040.83289510.6181611.4432140.342404-0.0807910.0
8442.29998856.64742313.845786100.0000001.014.7399900.650347158.4888801.0081793.0039983.69042231.7943230.9411260.3311550.0179361.0
9844.54998868.1187990-0.05325633.3333330.023.8400271.518001223.1550331.02085495.3744900.971870127.1666981.0076150.652236-0.0035521.0

Last rows

closeRSI_14INC_2ROC_2PSL_3CDL_DOJI_3_0.1TRUERANGE_1Z_30dif_M50M180ratio_M50M180dif_M5M20ratio_M5M20dif_M20M50ratio_M20M50ratio_MACDh_12_26_9obv_pct_deltaPL
37782.94000271.4012400-3.76850733.3333330.04.2239991.45487716.1667151.0284749.6870020.9524896.3260401.0388680.700051-0.0298400.0
378676.88000545.4038410-2.42889533.3333330.036.940002-0.741063250.1286770.987096-2.7660100.294475-83.9545961.0686221.041968-0.0006800.0
379850.45001268.70767812.94011366.6666670.022.2199711.471297227.5232781.01957587.8039920.920623126.6919980.9962670.5589360.0135171.0
380130.19000261.118926118.604698100.0000000.015.7999950.92918746.8856840.99094414.4854991.666686-30.5175400.9681871.3675770.0478431.0
381177.41200393.057545136.351203100.0000001.037.7980043.33446630.1503881.04826527.1363011.45229623.5815001.1037101.6990810.1430351.0
382421.26001050.1973400-3.84167333.3333330.031.500000-0.317897157.2327350.9881851.692503-0.4822642.8431000.8201170.640472-0.0149760.0
383655.90002467.80732513.57679033.3333330.039.3200071.346212162.0034901.00917641.1280110.93846098.9483991.040495-0.1922370.0037541.0
384816.03997880.012096111.009236100.0000000.061.0100102.797383189.4071561.02265075.8504881.266148112.0089011.0035192.0490760.0254801.0
385653.20001234.6252800-9.07951833.3333330.048.989990-2.092531275.4682880.990714-88.6715031.082852-6.551100-1.4976001.030990-0.0142870.0
38688.60199778.62739115.899645100.0000000.04.7480011.78575117.7349621.0325377.4596000.9236067.0987601.0518741.0902780.0361691.0